Currently, Internet resources are widely used in medical education, becoming one of the key tools of e-learning. We have developed a web application for congenital malformations and anomalies for medical students as an additional tool for self-learning. The web application contains two components: multimedia descriptions of congenital malformations, including images, animations, videos and interactive graphical tests; and the knowledge control module. It is important to evaluate effectiveness of web application and to improve the quality of the resources. We sought to evaluate the effectiveness of a web application by analyzing user knowledge dynamics and use the information to improve content. The anonymous testing involved 260 users – doctors, medical students and teachers. Using the longitudinal method of the study, we analyzed the dynamics of the group-averaged rate of correct responses after repeated testing attempts. The results showed differences in the initial level of knowledge of users, and the results of medical students’ tests in dynamics were improved more than the results of doctors. The initial value of the percentage of correct responses to the control questions and the growth dynamics of this indicator after repeated attempts are important indicators for assessing the usefulness of a particular question. For developers, this information, based on objective indicators, has proved useful for improving the educational resource.
References
1. Kobrinskij B.A. Komp'yuterizirovannye i distancionnye obuchayushchie sistemy ( na primere medicinskoj diagnostiki ). Otkrytoe obrazovanie. 2018 ; 22 ( 2 ): 45–53. (In Russ).
2. Khasawneh R., Simonsen K., Higgins J., Beck G. The effectiveness of e-learning in pediatric medical student education. Medical Education Online. 2016 ; 21: 29516. DOI: 10.3402/ meo.v 21.29516.
3. Ruiz J. G., Mintzer M. J., Leipzig R. M. The impact of e-learning in medical education. Academic Medicine. 2006 ; 81(3): 207–212.
4. Ito J., Tabei Y., Shimizu K. at al. PoSSuM : a database of similar protein-ligand binding and putative pockets. Nucleic Acids Research. 2012; 40(D1): D541-D548.
5. Guest S.S., Evans C.D., Winter R.M. The online London dysmorphology database. Genetics in Medicine Volume. 1999 ; 1(5): 207–212.
6. Ayme S., Caraboeuf M., Gouvernet J. GENDIAG – a computer-assisted syndrome identification system. Clinical Genetics. 1985 ; 28(5): 410–411.
7. Centers for Disease Control and Prevention. Specific Birth Defects. CDC. Available at: https://www.cdc.gov/ncbddd/ birthdefects /types.html. Last accessed on August, 14. 2020.
8. Putincev A.N., Alekseev T.V., Akimenkov A.M., Demikova N.S., Lapina A.S. Internet- prilozhenie « Vrozhdennye poroki razvitiya » dlya povysheniya urovnya znanij vrachej i obucheniya studentov. Rossijskij vestnik perinatologii i pediatrii. 2017 ; 62 ( 3 ): 130–136. (In Russ).
9. Boulmetis J., Dutwin P. The ABCs of Evaluation: Timeless Techniques for Program and Project Managers. 3rd ed., US. John Wiley & Sons, 2014.
10. Lyke J., Frank M. Comparison of student learning outcomes in online and traditional classroom environment psychology course. J Instruct Psychol. 2013: 39: 245–250.
11. Lahti M, Hätönen H., Välimäki M. Impact of e-learning on nurses’ and student nurses knowledge, skills, and satisfaction: a systematic review and meta-analysis. Int J Nurs Stud. 2014; 51(1): 136–49.
12. Voronina M.F., Karpova E.A. Modeli ocenki effektivnosti obucheniya v kontekste kompetentnostnogo podhoda. Sociologiya i pravo. 2 016 ; 1(31) : 27–37. (In Russ).
13. Praslova L. Adaptation Kirkpatrick’s four level model of training criteria to assessment of learning outcomes and program evaluation in Higher Education. Educational assessment , E valuation and A ccountability. 2010 ; 22(3): 215–225.
2. Khasawneh R., Simonsen K., Higgins J., Beck G. The effectiveness of e-learning in pediatric medical student education. Medical Education Online. 2016 ; 21: 29516. DOI: 10.3402/ meo.v 21.29516.
3. Ruiz J. G., Mintzer M. J., Leipzig R. M. The impact of e-learning in medical education. Academic Medicine. 2006 ; 81(3): 207–212.
4. Ito J., Tabei Y., Shimizu K. at al. PoSSuM : a database of similar protein-ligand binding and putative pockets. Nucleic Acids Research. 2012; 40(D1): D541-D548.
5. Guest S.S., Evans C.D., Winter R.M. The online London dysmorphology database. Genetics in Medicine Volume. 1999 ; 1(5): 207–212.
6. Ayme S., Caraboeuf M., Gouvernet J. GENDIAG – a computer-assisted syndrome identification system. Clinical Genetics. 1985 ; 28(5): 410–411.
7. Centers for Disease Control and Prevention. Specific Birth Defects. CDC. Available at: https://www.cdc.gov/ncbddd/ birthdefects /types.html. Last accessed on August, 14. 2020.
8. Putincev A.N., Alekseev T.V., Akimenkov A.M., Demikova N.S., Lapina A.S. Internet- prilozhenie « Vrozhdennye poroki razvitiya » dlya povysheniya urovnya znanij vrachej i obucheniya studentov. Rossijskij vestnik perinatologii i pediatrii. 2017 ; 62 ( 3 ): 130–136. (In Russ).
9. Boulmetis J., Dutwin P. The ABCs of Evaluation: Timeless Techniques for Program and Project Managers. 3rd ed., US. John Wiley & Sons, 2014.
10. Lyke J., Frank M. Comparison of student learning outcomes in online and traditional classroom environment psychology course. J Instruct Psychol. 2013: 39: 245–250.
11. Lahti M, Hätönen H., Välimäki M. Impact of e-learning on nurses’ and student nurses knowledge, skills, and satisfaction: a systematic review and meta-analysis. Int J Nurs Stud. 2014; 51(1): 136–49.
12. Voronina M.F., Karpova E.A. Modeli ocenki effektivnosti obucheniya v kontekste kompetentnostnogo podhoda. Sociologiya i pravo. 2 016 ; 1(31) : 27–37. (In Russ).
13. Praslova L. Adaptation Kirkpatrick’s four level model of training criteria to assessment of learning outcomes and program evaluation in Higher Education. Educational assessment , E valuation and A ccountability. 2010 ; 22(3): 215–225.
For citation
Documents
Keywords